Table of Content

    04 January 2023, Volume 0 Issue 12
    Dynamic Allocation Algorithm of Container Cloud Resources Based on Bi-level Programming
    ZHOU Yong-fu, XU Sheng-chao
    2022, 0(12):  1-5. 
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    The dynamic configuration decision problem of container cloud resources is analyzed in this paper. By defining the scheduling task of container cloud resources, the scheduling time of container source resources is solved. The shortest time matrix of container cloud resource scheduling task is used to obtain the conditions needed for container cloud resource scheduling. Under the bi-level planning condition, the objective function and constraint function of container cloud resource scheduling are solved, and the container cloud resource scheduling model is constructed. Considering the tasks of users and the cloud resources of data centers, a matrix to physical hosts is constructed on virtual machines. By constructing the objective function of container cloud resource dynamic configuration results in optimization, and combining with constraints, the dynamic configuration of container cloud resources is realized. Experimental results show that the proposed algorithm can not only improve the utilization of container cloud resources, but also reduce the configuration completion time, and has better dynamic configuration performance.
    Probabilistic Voltage Stability Evaluation Algorithm Based on Mixed Vine Copula and ILHS
    PENG Sui, XU Liang, ZHANG Zhi-qiang, LOU Yuan-yuan, YU Hao, QIN Xiao-hui
    2022, 0(12):  6-12. 
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    In order to evaluate the impact of solar and photovoltaic output with complex correlation on power system voltage stability, a probabilistic voltage stability evaluation (PVSE) algorithm based on mixed vine Copula and inherit Latin hypercube sampling (ILHS) is proposed. Based on fuzzy c-means clustering, the scenarios of wind speed and solar radiation data in the practical power grid is divided. The AD distance is used to evaluate the optimal vine structure in different scenarios, and a probability input model based on mixed vine Copula is established. The sample points are sampled from the probability input model by using the ILHS, and the number of sample points is gradually increased according to the convergence criterion until PVSE converges. At the same time, the previously generated sample points and their corresponding results are continuously reused in the process of probability analysis, so as to greatly improve the efficiency of probability analysis. Based on the IEEE-118 bus system, the effectiveness of the proposed algorithm is verified. The results show that the proposed algorithm can accurately describe the correlation structure of multi-dimensional input data, and the calculation accuracy and speed of PVSE can be greatly improved by using the proposed algorithm.
    Anomaly Detection of Student Consumption Data Based on Semi-supervised Learning
    SONG Xiao-li, ZHANG Yong-bo, ZHANG Pei-ying
    2022, 0(12):  13-17. 
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    With the more and more extensive application scenarios of campus card, the problem of capital security of campus card has become increasingly prominent. Campus card fraud will not only bring economic losses to teachers, students and businesses in the school, but also endanger the normal order of the campus. Aiming at the problem that the traditional anomaly detection method can not effectively extract the temporal feature of student consumption data, this paper proposes an anomaly detection method of student consumption data based on semi-supervised learning. Firstly, the auto-encoder is enhanced with the Gated Recurrent Unit, so that the model can reconstruct the consumption data more accurately. Then, the reconstruction error is calculated by Mahalanobis Distance, and the error threshold is determined by Fβ-Socre to detect abnormal data. Finally, the proposed method is used to detect the anomaly of student consumption data in a university. Experimental results show that the proposed method has better detection performance.
    Troposcatter Channel Estimation Based on Massive MIMO
    SHI Qing-lin, LIU Li-zhe, LI Xing-jian
    2022, 0(12):  18-25. 
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    With the increasing demand of users for communication speed, the communication capacity of tropospheric scattering communication needs to be improved. Massive multiple input multiple output (MIMO) technology is an important way to improve capacity. This paper studies the channel estimation problem of troposcatter communication system based on massive MIMO. Firstly, a massive MIMO troposcatter channel model based on two-dimensional uniform rectangular array is established. Secondly, a channel covariance matrix estimation algorithm is proposed to improve the traditional minimum mean square (MMSE) channel estimation algorithm. Finally, the accuracy of channel estimation algorithm is compared with that of least square (LS), traditional MMSE and ideal MMSE. The simulation results show that when the SNR is 0~25 dB, the accuracy of the traditional MMSE algorithm is not significantly improved compared with that of LS algorithm, and there is a certain gap between the accuracy of the ideal MMSE algorithm and that of the traditional MMSE algorithm. However, the accuracy of the improved MMSE channel estimation algorithm is better than that of the traditional MMSE algorithm. Under the same conditions, when the NMSE is the same, the SNR of the improved MMSE algorithm can be improved by 3~5 dB, and gradually approaches the ideal MMSE algorithm with the increase of SNR.
    Optimization of Plant Growth Simulation Algorithm for Vehicle Routing Problem with Time Windows
    WANG Kuo, HAO Fu-zhen
    2022, 0(12):  26-32. 
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    VRPTW problem is a vehicle routing problem with time window constraints. The solution of this problem is usually applied to the logistics path planning. It is of great practical significance and belongs to the NP problem. The amount of computation of VRPTW problem increases exponentially with the increase of data scale. Plant growth simulation algorithm (PGSA) is a heuristic algorithm that simulates plant growth information and branching patterns, and is used to solve combinatorial optimization problems. In this paper, a constraint model of VRPTW problem is constructed with the goal of minimizing the total distance of distribution. On the basis of the original PGSA, a two-stage search scheme is used to improve the quality of the initial solution. The directed growth mechanism and local solution jump-out mechanism are designed to change the growth strategy of the original algorithm, and the search efficiency of the PGSA algorithm is improved. Through the experimental analysis on the standard data set, the improved plant growth simulation algorithm can achieve better convergence results and higher efficiency than the original plant growth simulation algorithm, so it is an effective solution method.
    Byzantine Fault-tolerant Distributed Consistency Algorithm for Edge Computing Applications
    ZHANG Hao, LU Hong-ying
    2022, 0(12):  33-41. 
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    In order to solve the problem that edge nodes are easy to be attacked or captured to produce Byzantine errors and thus destroy the availability of edge computing applications, a Byzantine fault-tolerant distributed consistency algorithm Edge-Raft is designed for edge computing applications. The algorithm on the basis of the existing classical algorithm of Raft, under the conditions of edge, the Byzantine error of potential, with the introduction of digital signature, synchronous log detection, polling elections, inert vote, three phase synchronization mechanisms such as log, makes its have the Byzantine fault tolerance features at the same time, limits the complexity of the messaging to linear, ensures that the edge nodes with less than 1/3 of the total number of clusters can still provide effective services for users when Byzantine errors occur. Experimental results based on different node sizes show that compared with the existing Raft algorithm, the proposed algorithm retains the comprehensibility of Raft algorithm while ensuring the usability and activity of the proposed algorithm in the edge environment. Compared with the existing Practical Byzantine Fault Tolerance algorithms, the proposed algorithm limits the time complexity of message passing to the linear level, which ensures the scalability of the proposed algorithm in multi-node edge environment.
    Reliability Evaluation Model of BP Neural Network Based on Particle Swarm Optimization
    WANG Ying-ying, ZHUANG Yi, SUN Yi-fan
    2022, 0(12):  42-49. 
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    The reliability of the CPU is critical to a computer system. For the problem that the difficulty of parameter optimization and inaccurate evaluation accuracy in reliability analysis and evaluation of methods such as neural network, this paper proposes a reliability evaluation model based on particle swarm optimization BP neural network. The model optimized the PSO algorithm optimized by the sine map, and then optimized the weights and thresholds of the BP neural network. Through this method, the weights and thresholds of the BP neural network were optimized. Based on the reliability of each functional module in the CPU, a reliability evaluation model of the CPU was established according to the improved BP neural network model. The reliability evaluation of the CPU was completed through model training and testing. Through comparative experiments, the validity and accuracy of the model for CPU reliability evaluation under radiation environment are verified.
    Automatic Sleep Staging Algorithm Based on Self-attention Mechanism and Single Lead ECG
    LI Wei-song, TANG Min-fang, HE Zheng-ling, WANG Peng, DU Li-dong, FANG Zhen, CHEN Xian-xiang
    2022, 0(12):  50-59. 
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    The realization of sleep staging based on manual labeling or traditional machine learning methods is complex and inefficient. Deep neural network improves the results of sleep staging because of its powerful ability to extract complex features, but there are still some problems, such as ignoring the correlation of internal information. To solve this problem, this paper proposes an automatic sleep staging algorithm based on self-attention mechanism and single lead ECG signal, realizing feature extraction and classification automatically by using convolution module, bidirectional gated recurrent unit and self-attention mechanism. In the open database Sleep Heart Health Study database (SHHS1, SHHS2), Multi-Ethnic Study of Atherosclerosis database (MESA) and MIT-BIH Polysomnographic database (MITBPD), the single lead ECG data of 1000, 1000, 1000 and 16 subjects are randomly selected for training and testing. The experimental results show that the accuracy of the four sleep classifications (wake, rapid eye movement, light sleep and deep sleep) of the model is 75.77% (kappa=0.63), 81.01% (kappa =0.66), 8279% (kappa=0.71) and 76.22% (kappa=0.58) respectively, which is better than the sleep staging results based on the traditional machine learning algorithms, verifying the validity of the model.
    Pump Detection Period Predicting of Pump Well Based on Feature Fusion
    ZHANG Xiao-dong, WANG Xu-ying, QIN Zi-xuan
    2022, 0(12):  60-66. 
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    Pump detection period is an important index to reflect the working reliability of pumping wells. Accurate prediction of pump detection period is of great practical significance to improve oil well production efficiency and economic benefits. Aiming at the low accuracy of pump detection period prediction in oil field, a pump detection period prediction method based on feature fusion is proposed. This method introduces SVR to extract the static characteristics of oilfield data, reconstructs the characteristics of oilfield dynamic data, uses convolution neural network to learn the dynamic characteristics of oilfield data, introduces multi-modal compression bilinear pooling to fuse the static and dynamic characteristics, and uses discriminant model to train the fusion characteristics to realize the accurate prediction of pump detection cycle. The experimental results verify the effectiveness and feasibility of the model.
    Multispectral Image Classification Based on Context-aware and Super-pixel Post-processing
    WU Zhi-ping, MA Yao-bin, TANG Wen-chao, HU Bi-wei, HU Bi-wei, LIU Ming-jia
    2022, 0(12):  67-73. 
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    To extract ground content is the basis for a large number of geoscientific applications. Existing pixel-based classification methods do not fully exploit the contextual associations in multispectral remote sensing images, and fragmented labels are observed everywhere in classified images. In order to improve the classification accuracy of high-resolution multispectral images, this paper proposes a new method which is based on context-aware networks and super-pixel post-processing. The method designs a new convolutional neural network to learn the spatial contextual information in multispectral images. Super-pixel post-processing uses a strategy of small region segmentation and voting to merge structurally associated regions, which can eliminate fragmented labels. The new method is tested on the Gaofen-1 satellite data and compared with six classification algorithms. The experimental results show that the new method outperforms the competing algorithms in terms of accuracy and visual effect. Among them, the super-pixel post-processing can reduce the fragmentation of classification results as well as improve the classification accuracy.
    Composite Object Detection Based on Improved YOLOv3 from High-resolution Remote Sensing Image
    ZHANG Biao, WANG Hui-xian, HAN Bing,
    2022, 0(12):  74-80. 
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    Compared with a single object, the composite object of remote sensing image has multiple structures, and there are certain differences between the structures. The composite object has the characteristics of variability and complexity; the remote sensing image is wide and the background is complex, and there are many areas similar to the characteristics of the composite object to be inspected. The above two points lead to the low accuracy of the composite object detection. In response to this problem, this article develops research on composite object detection based on high-resolution remote sensing images. This paper first carries out object characteristic analysis and sample data labeling; then proposes an improved YOLOv3 detection algorithm based on Coordinate Attention attention mechanism and Focal Loss function; finally, an experiment is carried out with a composite target of a basketball court as an example. The experimental results show that compared with the original YOLOv3 algorithm, the recall rate and average detection accuracy of the improved algorithm are increased by 10.3 percentage points and 28.8 percentage points, respectively. The result verifies the feasibility and rationality of the proposed scheme.
    Face Clustering Method Based on Nearest Neighborhood Aggregation
    WEN Zi-xin, LI Shao-ying, WANG Bin-cheng, LIU Bo,
    2022, 0(12):  81-87. 
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    Face clustering is a pre-processing process for face annotation, face recognition and other tasks. It can reduce the labelling burden and provide high-quality annotation for face recognition models by grouping face images. The challenge of face clustering is to extract the global and local structural knowledge in large-scale face datasets and transfer it to the unlabelled ones. To address the issue, a face clustering method based on nearest neighbor aggregation is proposed. The method formulates local structure learning as a link prediction problem. It extracts multi-scale neighborhood characteristics by multiple improved residual Fully-Connected block. The experimental results show that the proposed method can effectively improve the clustering accuracy on the benchmark compared with the mainstream face clustering methods.
    Remote Sensing Image Object Detection Based on Improved MoCo
    JIAO Xin-quan, LI Rui-kang, CHEN Jian-jun
    2022, 0(12):  88-94. 
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    In the intelligent processing of satellite remote sensing images, there are some problems such as inconsistent standards and uneven data distribution, resulting in few effective samples and poor object detection effect. Aiming at this phenomenon, an object detection algorithm based on MoCo unsupervised contrast learning model is proposed. The framework of object detection adopts YOLOv5 with ResNet50 as the backbone network, and the weight of ResNet50 obtained by contrastive learning is used as a fixed value to participate in the detection task training of YOLOv5 downstream without gradient iteration. The contrastive learning experiment is carried out on AID Dataset, and the top-1 accuracy of the improved MoCo v2 is 95.888%. In the downstream detection task, using the TGRS-HRRSD Dataset, the accuracy of mAP@.5:.95 with the improved MoCo v2 pre-training weight is 67.8%, which is 5.6 percentage points higher than that without the pre-training weight. The results show that the improved MoCo comparative learning model is effective, and the detection accuracy is also improved in the downstream detection tasks after the comparative learning. 
    Parallax Image Stitching Algorithm Based on GMS and Improved Optimal Seam
    LI Si-jie, TANG Qing-shan, GAO Ying-hua
    2022, 0(12):  95-101. 
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    Aiming at the problems of ghost and uneven brightness in parallax image stitching, this paper proposes a parallax image stitching algorithm based on grid motion statistics(GMS) and improved optimal seam. Firstly, the fast feature extraction and description(ORB) algorithm is used to extract feature points and the GMS algorithm is used to screen out the mismatched points. Then, HSV color space and image gradient difference are introduced to improve the energy function to avoid the stitching line passing through the image edge. Based on the graph cutting method, the optimal seam is obtained, and the gradient fusion stitching of the image is carried out. The simulation results show that, in the case of large disparity, compared with the algorithm based on scale feature invariance(SIFT) and the algorithm based on accelerated robustness feature(SURF), the accuracy of feature point matching of this algorithm is increased by 2.01 times and 4.73 times at the lowest and highest, and the image naturalness is increased by 20.6% on average. Moreover, the stitched image has uniform brightness and no perspective distortion.
    A Trusted Transmission Scheme of SDN Based on Path Tracking Feedback
    GAO Feng, ZHUANG Yi, LIU Xiao
    2022, 0(12):  102-110. 
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    To address the problems with software defined network, such as the inevitable loopholes in the forwarding equipment and the lack of mechanisms for the controller to actively check network behaviors, a trusted transmission scheme of the SDN based on path tracking feedback is proposed. A transmission path trust verification mechanism based on tracking feedback is proposed in the scheme. Based on the feedback information, the credibility of the node is analyzed and the credibility of the path is evaluated. At the same time, a disjoint multi-path trusted routing algorithm DMTRA-PTF based on path tracking feedback is proposed to avoid malicious switch nodes through path tracking feedback and trusted evaluation, so as to construct disjoint multipath routing scheme to enhance the reliability of SDN transmission service. The experimental results show that the path tracking feedback mechanism can accurately identify the malicious switch with a small performance cost, and the trusted routing algorithm proposed in this paper can dynamically plan disjoint multiple trusted paths for subsequent routes, which can effectively improve the credibility of the whole network.
    OpenID Protocol Based on SM9 Blind Signature
    WANG Xuan, WANG Zhi-wei,
    2022, 0(12):  111-117. 
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    OpenID is a user-centered digital identity recognition framework and a decentralized online identity authentication system. It has the characteristics of openness, decentralization and freedom. However, some existing OpenID protocols still have many deficiencies in effectively protecting user privacy. For example, identity providers can learn the relying party information logged in by users through each use. In view of the above problems, a design idea of the OpenID protocol based on blind signature is proposed, which blinds the website identifier of the OpenID relying party. This paper first designs an identity-based blind signature scheme based on the national secret algorithm SM9, and proves that the security of this scheme depends on SM9 signature scheme. Then, based on the above blind signature scheme, an OpenID protocol is designed. Finally, the efficiency and security of the proposed OpenID protocol are demonstrated through simulation experiments and theoretical analysis.
    Secure Data Access Method of Industrial Internet Based on Fog Computing
    LI Jing-yuan, ZHANG Ke, YANG Dong-yu
    2022, 0(12):  118-122. 
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    Aiming at the requirements of low delay, low power consumption and security in the industrial Internet application scenario, a secure access method of industrial Internet data based on fog computing architecture is proposed. The corresponding asymmetric key pair is generated based on the attribute set to encrypt the message and stored in the cloud server. Part of the encryption and decryption tasks of the ciphertext are completed by the fog node layer, which eliminates trust dependency in the cloud service layer and reduces the computing overhead burden of the device layer. The fog node layer and cloud service layer are semi trusted to the ciphertext data. They cannot obtain any original message according to the ciphertext. Only the authorized device can complete the complete decryption and obtain the original message by using the private key, so as to realize the end-to-end efficient and secure data access in the industrial Internet. The performance analysis shows that the proposed method has lower computational overhead and response delay and more reliable security and privacy than other schemes.